Lung Cancer Cell Sing Cell RNA Seq Analysis

Figure 1: Vlnplot, the number of genes, UMIs, and the percentage of mitochondrial genes

While the CreateSeuratObject imposes a basic minimum gene-cutoff, you may want to filter out cells at this stage based on technical or biological parameters. Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. In the example below, we visualize gene and molecule counts, plot their relationship, and exclude cells with a clear outlier number of genes detected as potential multiplets. Of course this is not a guaranteed method to exclude cell doublets, but we include this as an example of filtering user-defined outlier cells. We also filter cells based on the percentage of mitochondrial genes present.

Figure 2: GenePlot, the number UMIs VS the percentage of mitochondrial genes

GenePlot is typically used to visualize gene-gene relationships, but can be used for anything calculated by the object, i.e. columns in meta.data, PC scores etc. Since there is a rare subset of cells with an outlier level of high mitochondrial percentage and also low UMI content, we filter these cells

Figure 3: GenePlot, the number UMIs VS the number of Gene

Figure 4: Find Variable genes

Seurat calculates highly variable genes and focuses on these for downstream analysis. FindVariableGenes calculates the average expression and dispersion for each gene, places these genes into bins, and then calculates a z-score for dispersion within each bin. This helps control for the relationship between variability and average expression. This function is unchanged from (Macosko et al.), but new methods for variable gene expression identification are coming soon. We suggest that users set these parameters to mark visual outliers on the dispersion plot, but the exact parameter settings may vary based on the data type, heterogeneity in the sample, and normalization strategy. The parameters here identify ~2,000 variable genes, and represent typical parameter settings for UMI data that is normalized to a total of 1e4 molecules.

Figure 5: PCA analysis

Next we perform PCA on the scaled data. By default, the genes selected are used as input, but can be defined using pc.genes. We have typically found that running dimensionality reduction on highly variable genes can improve performance. However, with UMI data - particularly after regressing out technical variables, we often see that PCA returns similar (albeit slower) results when run on much larger subsets of genes, including the whole transcriptome.

Figure 6: Projection of cells in PC1 and PC2 space

Figure 7: Heatmap of Variable genes in PC1

In particular PCHeatmap allows for easy exploration of the primary sources of heterogeneity in a dataset, and can be useful when trying to decide which PCs to include for further downstream analyses. Both cells and genes are ordered according to their PCA scores. Setting cells.use to a number plots the ‘extreme’ cells on both ends of the spectrum, which dramatically speeds plotting for large datasets. Though clearly a supervised analysis, we find this to be a valuable tool for exploring correlated gene sets.

Figure 8: Heatmap of Variable genes in PC1~PC12

In particular PCHeatmap allows for easy exploration of the primary sources of heterogeneity in a dataset, and can be useful when trying to decide which PCs to include for further downstream analyses. Both cells and genes are ordered according to their PCA scores. Setting cells.use to a number plots the ‘extreme’ cells on both ends of the spectrum, which dramatically speeds plotting for large datasets. Though clearly a supervised analysis, we find this to be a valuable tool for exploring correlated gene sets.

Figure 9: Elbow plot for PC selection

PC selection – identifying the true dimensionality of a dataset – is an important step for Seurat, but can be challenging/uncertain for the user. We therefore suggest these three approaches to consider. The first is more supervised, exploring PCs to determine relevant sources of heterogeneity, and could be used in conjunction with GSEA for example. The second implements a statistical test based on a random null model, but is time-consuming for large datasets, and may not return a clear PC cutoff. The third is a heuristic that is commonly used, and can be calculated instantly. In this example, all three approaches yielded similar results, but we might have been justified in choosing anything between PC 7-10 as a cutoff. We followed the jackStraw here, admittedly buoyed by seeing the PCHeatmap returning interpretable signals (including canonical dendritic cell markers) throughout these PCs. Though the results are only subtly affected by small shifts in this cutoff (you can test below), we strongly suggest always explore the PCs they choose to include downstream.

Figure 10: TSNE plot over predicted clusters

Seurat continues to use tSNE as a powerful tool to visualize and explore these datasets. While we no longer advise clustering directly on tSNE components, cells within the graph-based clusters determined above should co-localize on the tSNE plot. This is because the tSNE aims to place cells with similar local neighborhoods in high-dimensional space together in low-dimensional space. As input to the tSNE, we suggest using the same PCs as input to the clustering analysis, although computing the tSNE based on scaled gene expression is also supported using the genes.use argument.

Figure 11: Dotplot of predicted marker genes

Seurat can help you find markers (top 5) that define clusters via differential expression. By default, it identifes positive and negative markers of a single cluster (specified in ident.1), compared to all other cells. FindAllMarkers automates this process for all clusters, but you can also test groups of clusters vs. each other, or against all cells.

Figure 12: Differential expression analysis